Hardware Implementation of Differential Oscillatory Neural Networks using VO2-Based Oscillators and Memristor-Bridge Circuits J. Shamsi, M.J. Avedillo, B. Linares-Barranco and T. Serrano-Gotarredona Journal Paper · Frontiers in Neuroscience, vol. 15, article 674567, 2021 resumendoi
Oscillatory Neural Networks (ONNs) are currently arousing interest in the research community for their potential to implement very fast, ultra-low-power computing tasks by exploiting specific emerging technologies. From the architectural point of view, ONNs are based on the synchronization of oscillatory neurons in cognitive processing, as occurs in the human brain. As emerging technologies, VO2 and memristive devices show promising potential for the efficient implementation of ONNs. Abundant literature is now becoming available pertaining to the study and building of ONNs based on VO2 devices and resistive coupling, such as memristors. One drawback of direct resistive coupling is that physical resistances cannot be negative, but from the architectural and computational perspective this would be a powerful advantage when interconnecting weights in ONNs. Here we solve the problem by proposing a hardware implementation technique based on differential oscillatory neurons for ONNs (DONNs) with VO2-based oscillators and memristor-bridge circuits. Each differential oscillatory neuron is made of a pair of VO2 oscillators operating in anti-phase. This way, the neurons provide a pair of differential output signals in opposite phase. The memristor-bridge circuit is used as an adjustable coupling function that is compatible with differential structures and capable of providing both positive and negative weights. By combining differential oscillatory neurons and memristor-bridge circuits, we propose the hardware implementation of a fully connected differential ONN (DONN) and use it as an associative memory. The standard Hebbian rule is used for training, and the weights are then mapped to the memristor-bridge circuit through a proposed mapping rule. The paper also introduces some functional and hardware specifications to evaluate the design. Evaluation is performed by circuit-level electrical simulations and shows that the retrieval accuracy of the proposed design is comparable to that of classic Hopfield Neural Networks.
Oscillatory Hebbian Rule (OHR): An adaption of the Hebbian rule to Oscillatory Neural Networks J. Shamsi, M.J. Avedillo and B. Linares-Barranco Conference · Conference on Design of Circuits and Integrated Systems DCIS 2020 resumen
Hebbian rule plays an important role in training of artificial neural networks. According to this rule, a synaptic weight between two neurons is increased or decreased depending on the activity of the presynaptic and postsynaptic neurons. In this paper, an oscillatory version of the Hebbian rule is proposed for ONNs and is called Oscillatory Hebbian Rule (OHR). OHR simply expresses the weight change as a function of the phase difference between the presynaptic and postsynaptic neurons. Similar to STDP that weight change is an exponential function of the time difference between the presynaptic and postsynaptic spikes, OHR relates weight change to the phase difference between the presynaptic and postsynaptic neurons using exponential functions. Specifically, when two neurons are in-phase, the weight between them is increased while a weight between two anti-phase neurons is decreased. Simulation results show the capability of OHR for both supervised and unsupervised learning. In supervised learning, a basic block of feedforward architectures is trained as a classifier. When the basic block is used in unsupervised mode, it is capable to learn patterns while the output phase is converged to a specific phase.